| Computer Technology |
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| Recommendation method based on social topology for cold start users |
| ZHANG Ya nan,QU Ming cheng,LIU Yu peng |
1. Software school,Harbin University of Science and Technology,Harbin 150040,China；
2. School of computer science and Technology, Harbin Institute of Technology,Harbin 150001, China |
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Abstract It is very difficult to give recommendations for coldstart user who usually has very sparse historical behavior records. A coldstart recommendation method was proposed based on comparison of topology of social relationships in social networks to improve recommendation effectiveness for coldstart user. Social network contains many social relationships which could reflect users preference. However, most of existing social network based recommendation methods use only one or a few social relationships of social network, which do not make full use of multiple social relationships; rarely consider how to merge dissimilar social relationships, and could not give satisfactory recommendation in actual environment. In social network the higher weight a kind of social relationship takes, the greater right of recommendations it will have. In order to give accurate recommendations for coldstart user, a social topology based similar user matching method (STSUM) was proposed, Maximum entropy principle was introduced to merge multiple social relationships, and graph pattern matching was used to find similar users for coldstart user. Then recommendations were given according to similar users records. Social relationship and user data from a real website to show the recommendation effectiveness of STSUM. The experimental results show that STSUM con give accurate recommendations for coldstart user and needs a few training set．
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Published: 14 January 2017
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基于社交关系拓扑结构的冷启动推荐方法
针对冷启动用户仅有很少行为信息,很难为冷启动用户给出推荐的问题,提出基于比较社交网络中用户间社交关系拓扑结构的冷启动推荐方法.社交网络中包含多种可以反映用户偏好的社交关系,然而现有基于社交网络的冷启动推荐研究仅利用一种或者很少的社交关系,没有充分利用社交网络中的多种社交关系,很少考虑融合相异的社交关系,限制了在实际环境中对冷启动用户的推荐效果.由于社交关系在社交网络中的权重越大在推荐中的影响越大,为了给出准确的冷启动推荐,提出基于社交关系拓扑的相似用户发现方法(STSUM),基于最大熵原理融合社交网络中多种相异的社交关系,基于图形模式匹配为冷启动用户发现相似用户,给出推荐.在真实的网站中提取社交关系和用户数据,实验结果表明,STSUM可以有效地提高对冷启动用户的推荐效果且需要较少的训练集．
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